ArticlePloS one2015
miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Application of Artificial Intelligence Technology in Plant MicroRNA Research: Progress, Challenges, and Prospects.International journal of molecular sciences · 2025Review
- OrchidBase 5.0: updates of the orchid genome knowledgebase.BMC plant biology · 2022Article
- Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA.Genomics, proteomics & bioinformatics · 2022Article
- Machine learning: its challenges and opportunities in plant system biology.Applied microbiology and biotechnology · 2022Review
- Genomics and transcriptomics to protect rice (Frontiers in plant science · 2022Review
- Long noncoding RNA ZFAS1 promotes tumorigenesis and metastasis in nasopharyngeal carcinoma by sponging miR-892b to up-regulate LPAR1 expression.Journal of cellular and molecular medicine · 2020Article
- Computational tools for plant small RNA detection and categorization.Briefings in bioinformatics · 2019Review
- Development of species specific putative miRNA and its target prediction tool in wheat (Triticum aestivum L.).Scientific reports · 2019Article
- Transcriptome-Wide Annotation of mFrontiers in plant science · 2018Article
- A Comprehensive Prescription for Plant miRNA Identification.Frontiers in plant science · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify primary miRNAs and precursor miRNAs (pre-miRNAs), very few provide the functionality of locating mature miRNAs within plant pre-miRNAs. This manuscript introduces a novel algorithm for predicting miRNAs named miRLocator, which is based on machine learning techniques and sequence and structural features extracted from miRNA:miRNA* duplexes. To address the class imbalance problem (few real miRNAs and a large number of pseudo miRNAs), the prediction models in miRLocator were optimized by considering critical (and often ignored) factors that can markedly affect the prediction accuracy of mature miRNAs, including the machine learning algorithm and the ratio between training positive and negative samples. Ten-fold cross-validation on 5854 experimentally validated miRNAs from 19 plant species showed that miRLocator performed better than the state-of-art miRNA predictor miRdup in locating mature miRNAs within plant pre-miRNAs. miRLocator will aid researchers interested in discovering miRNAs from model and non-model plant species.
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Registered trials
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